
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "54"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = os.environ["ANTENNA"]
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "189"
csv_folder = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H5C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, '*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 75 csvs in /home/obs/src/H5C_Notebooks/_rtp_summary_
Found 71 auto_metrics notebooks in /home/obs/src/H5C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        antnum = row['Ant']
        if antnum != int(antenna):
            continue
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


html_to_display = ''
for am_html in auto_metric_htmls:
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line: 
            break


HTML(html_to_display)

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459594	digital_ok	0.00%	0.00%	0.00%	0.00%	2.16%	30.27%	0.440364	-0.008392	-0.449866	1.384387	-1.001894	0.068053	-0.066023	0.612749	0.7541	0.7645	0.3019	5.708775	4.796477
2459593	digital_ok	0.00%	0.00%	0.00%	0.00%	4.84%	14.52%	1.848311	1.375841	-0.323088	1.096857	-0.834459	0.099931	-0.007423	-0.029936	0.6892	0.6844	0.2969	1.745689	1.475627
2459592	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	-0.326950	-0.744112	0.065185	-0.686894	1.393294	2.537667	2.232199	-0.076113	0.0337	0.0321	0.0021	nan	nan
2459591	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.783302	1.620308	0.469731	-0.203760	-0.670923	14.138325	0.268803	0.555141	0.6463	0.6597	0.3974	4.783033	4.328172
2459590	digital_ok	0.00%	0.00%	0.00%	0.00%	1.62%	0.00%	2.444152	1.501515	0.308458	1.766354	-1.056578	0.787638	0.333437	-0.279942	0.6413	0.6490	0.3984	1.398333	1.212553
2459589	digital_ok	0.00%	0.00%	0.00%	0.00%	2.69%	27.96%	1.420405	1.018879	0.027363	2.049012	-0.033763	1.614770	0.069992	0.538192	0.6849	0.6787	0.3192	1.574068	1.382293
2459588	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.098191	1.892444	-0.569487	0.856545	-0.591293	52.470754	0.080508	1.416672	0.7160	0.6964	0.2843	8.024947	5.951849
2459587	digital_ok	100.00%	-	-	-	100.00%	0.00%	2.021677	1.665973	0.757719	3.151068	0.649980	2.561047	0.375163	0.205987	nan	nan	nan	28.002632	25.584155
2459586	digital_ok	0.00%	0.00%	0.00%	0.00%	5.41%	12.97%	1.130870	0.183638	0.823825	-0.642047	0.162500	1.468886	0.656410	-0.041170	0.6493	0.6590	0.3728	1.377451	1.366517
2459585	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	5.758159	-0.448296	1.776215	0.112515	2.676073	2.970319	6.936746	0.572965	0.0367	0.0313	0.0028	0.000000	0.000000
2459584	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	19.370099	19.138724	6.212853	7.292196	4.131056	18.441595	-0.365855	-0.301419	0.6055	0.6228	0.3945	32.971838	26.269567
2459583	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459582	digital_ok	0.00%	0.00%	0.00%	0.00%	39.46%	10.27%	1.310562	1.592580	2.014561	3.220029	-0.382844	-0.105129	0.204510	-1.031426	0.6504	0.6627	0.4117	0.980758	0.849658
2459581	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.516742	1.903018	2.732220	1.706071	0.352993	24.051639	0.760740	-0.595593	0.6508	0.6644	0.4107	1.670422	1.627115
2459580	digital_ok	0.00%	0.00%	0.00%	0.00%	3.76%	12.90%	1.529663	0.897285	2.258657	0.010411	-0.544217	1.639939	0.146789	-0.319692	0.6547	0.6686	0.4051	0.884393	0.724063
2459579	digital_ok	0.00%	0.00%	0.00%	0.00%	5.91%	0.00%	1.289769	1.466127	2.838289	3.787202	-0.249608	1.891409	1.026462	-0.984649	0.6471	0.6580	0.4102	0.388733	0.368300
2459578	digital_ok	100.00%	100.00%	100.00%	0.00%	-	-	-0.410514	1.382019	-0.662654	1.740676	1.305245	1.848006	10.432079	-0.329478	0.0349	0.0330	0.0030	nan	nan
2459577	digital_ok	0.00%	0.00%	0.00%	0.00%	4.32%	7.03%	2.224656	2.469850	2.369263	3.561400	0.755334	1.505459	0.466149	-0.703372	0.6497	0.6647	0.4070	0.735031	0.626226
2459576	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459575	digital_ok	0.00%	0.00%	0.00%	0.00%	4.30%	95.70%	1.391985	1.178683	1.535489	2.332889	0.756895	-0.095759	2.021121	1.772451	0.7464	0.7455	0.3559	23.669250	24.520210
2459574	digital_ok	0.00%	0.00%	0.00%	0.00%	11.35%	2.16%	1.425609	1.674268	1.691817	2.187902	1.080878	1.516491	0.341897	-0.676425	0.6637	0.6744	0.4176	1.263607	1.192103
2459573	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.671691	1.896290	3.054994	4.951015	0.348616	0.640849	0.525013	-0.384509	0.6678	0.6821	0.4116	14.289340	11.081895
2459572	digital_ok	0.00%	0.00%	0.00%	0.00%	27.57%	8.65%	1.310217	1.767193	1.659694	2.462196	0.253569	0.403493	1.223221	-1.388216	0.6739	0.6880	0.4093	0.879407	0.862931
2459571	digital_ok	100.00%	100.00%	100.00%	0.00%	-	-	-0.896943	1.094399	-0.939845	2.326796	0.913641	12.437881	12.790576	22.553263	0.0354	0.0387	0.0038	nan	nan
2459570	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	7.039771	4.061502	10.753512	3.844395	6.376013	4.224979	4.329659	2.112341	0.6579	0.6886	0.4011	0.000000	0.000000
2459569	digital_ok	0.00%	0.00%	0.00%	0.00%	4.32%	30.27%	1.072333	1.441065	2.178164	3.016876	2.108354	2.267072	2.206192	2.399831	0.7546	0.7590	0.2557	1.866369	2.007536
2459566	digital_ok	0.00%	0.00%	0.00%	0.00%	4.32%	95.68%	1.444440	1.562574	2.593424	3.319242	0.297325	1.120239	0.254144	-0.499869	0.6708	0.6850	0.4222	11.728582	9.734470
2459565	digital_ok	0.00%	-	-	-	93.33%	0.00%	1.074879	1.074879	0.972728	0.972728	0.664032	0.664032	0.146470	0.146470	nan	nan	nan	0.000000	0.000000
2459564	digital_ok	0.00%	0.00%	0.00%	0.00%	5.20%	0.00%	1.426075	-0.280658	1.097801	-2.627350	2.357538	3.426808	0.705949	0.718128	0.6806	0.6899	0.4192	1.851385	1.588684
2459563	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	94.62%	1.650922	1.064608	1.560642	0.886993	-0.779731	1.944344	0.712884	-0.339835	0.6795	0.6905	0.4138	12.020165	11.927929
2459562	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	0.984171	-0.936049	1.549735	-2.514602	0.911262	-0.895447	0.712321	-0.684886	0.6817	0.6969	0.4099	1.943242	1.779023
2459561	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.939523	-0.409054	1.330409	-1.948638	0.575240	5.187639	0.905793	0.984151	0.6784	0.6947	0.4160	3.445794	3.065594
2459560	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	7.03%	1.095876	-0.505389	1.527647	-1.949280	0.855659	0.635452	0.873444	0.495481	0.6789	0.6917	0.4189	1.791674	1.621350
2459559	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	1.08%	1.124425	-0.672789	1.503797	-2.141796	0.675080	-0.018089	0.558401	-0.785300	0.6798	0.6947	0.4032	1.891050	1.705304
2459558	digital_ok	0.00%	0.00%	0.00%	0.00%	7.53%	6.99%	1.720452	1.744789	2.277665	3.618058	-0.610600	0.230745	0.189609	-0.376374	0.6839	0.6933	0.4109	1.017839	0.868704
2459557	digital_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0320	0.0320	0.0013	nan	nan
2459556	digital_ok	0.00%	0.00%	0.00%	0.00%	7.03%	0.00%	1.751857	2.048452	2.450472	3.815426	0.635497	0.769088	1.871087	-0.675328	0.6709	0.6838	0.4215	1.013619	0.877176
2459554	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.6848	0.6963	0.4193	nan	nan
2459553	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.6829	0.6946	0.4170	nan	nan
2459552	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.726923	2.971931	3.638079	6.692545	0.470107	2.722005	0.442434	0.099110	0.6855	0.6945	0.4189	8.134972	7.951641
2459551	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.00%	0.972131	-0.584702	1.753696	-2.737047	1.259724	-0.740106	0.380284	-0.927107	0.6808	0.6954	0.4122	1.988407	1.714987
2459550	digital_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0309	0.0313	0.0013	nan	nan
2459549	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.669958	1.954605	2.816128	4.258278	0.630722	1.602857	0.274700	-1.030611	0.6802	0.6940	0.4216	2.588676	2.411188
2459542	digital_ok	0.00%	0.00%	0.00%	0.00%	22.32%	0.00%	2.334226	1.731882	3.445583	3.826931	0.545417	1.569019	-0.002489	-0.950000	0.7480	0.7516	0.3655	1.021782	0.816775
2459541	digital_ok	100.00%	1.79%	1.79%	0.00%	100.00%	0.00%	2.647371	2.105582	2.912713	5.696218	5.269294	3.924767	1.874364	1.947970	0.7284	0.7317	0.3535	0.000000	0.000000
2459540	digital_ok	0.00%	0.00%	0.00%	0.00%	26.79%	8.48%	1.275635	0.021820	1.349630	-1.643468	0.620397	1.965236	0.230061	-0.856906	0.7450	0.7460	0.3608	0.612266	0.599668
2459536	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.781058	3.997386	5.405218	8.536078	1.990690	9.228014	1.453155	-0.508101	0.6985	0.7130	0.4490	0.000000	0.000000
2459535	digital_ok	0.00%	0.00%	0.00%	0.00%	98.66%	1.34%	1.118909	-0.130826	1.381960	-1.704223	0.993082	0.461758	0.989520	1.225883	0.7661	0.7659	0.3748	0.983784	0.980572
2459534	digital_ok	0.00%	0.00%	0.00%	0.00%	97.77%	2.23%	0.916443	-0.108715	1.341109	-1.518653	0.886558	1.136407	0.781497	2.085900	0.7576	0.7491	0.3815	-0.000000	-0.000000
2459533	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459532	digital_ok	0.00%	0.00%	0.00%	0.00%	99.44%	0.56%	2.360559	2.028106	3.629762	3.395268	0.033099	2.451213	-0.023437	1.171902	0.6955	0.7134	0.4281	-0.000000	-0.000000
2459530	digital_ok	0.00%	0.00%	0.00%	0.00%	98.82%	1.18%	1.126633	-0.479310	1.300108	-2.174334	0.934683	-0.461014	0.613313	-0.003120	0.7108	0.7234	0.4259	0.842311	0.831415
2459527	digital_ok	0.00%	-	-	-	100.00%	0.00%	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459524	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459523	digital_ok	100.00%	-	-	-	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459522	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459513	digital_ok	100.00%	100.00%	100.00%	0.00%	-	-	-0.884474	1.486842	-0.773101	1.079406	2.876613	4.158975	16.058948	-0.166893	0.0374	0.0357	0.0031	nan	nan
2459508	digital_ok	0.00%	0.00%	0.00%	0.00%	81.58%	0.00%	2.196789	1.423239	2.095768	1.726201	2.216320	1.596833	1.670036	2.447798	0.9132	0.9527	0.1834	0.000000	0.000000
2459505	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	2.747121	1.682121	3.045865	3.516356	0.563626	0.338591	0.608271	-0.854600	0.8885	0.8540	0.2012	5.314490	4.007841
2459503	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	3.384912	3.859870	3.229931	4.240978	5.686435	7.224951	1.887885	-0.085898	0.0473	0.0905	0.0060	0.000000	0.000000

Antenna Season Report Notebook¶

Antenna 189 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 189, Node 15:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

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Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶